%20on%20human-generated%20data%20remains%20a%20prevalent%20practice.%20However%2C%20the%20performance%20of%20such%20models%20is%20often%20limited%20by%20the%20quantity%20and%20diversity%20of%20high-quality%20human%20data.%20In%20this%20paper%2C%20we%20explore%20whether%20we%20can%20go%20beyond%20human%20data%20on%20tasks%20where%20we%20have%20access%20to%20scalar%20feedback%2C%20for%20example%2C%20on%20math%20problems%20where%20one%20can%20verify%20correctness.%20To%20do%20so%2C%20we%20investigate%20a%20simple%20self-training%20method%20based%20on%20expectation-maximization%2C%20which%20we%20call%20ReST%24%5E%7BEM%7D%24%2C%20where%20we%20(1)%20generate%20samples%20from%20the%20model%20and%20filter%20them%20using%20binary%20feedback%2C%20(2)%20fine-tune%20the%20model%20on%20these%20samples%2C%20and%20(3)%20repeat%20this%20process%20a%20few%20times.%20Testing%20on%20advanced%20MATH%20reasoning%20and%20APPS%20coding%20benchmarks%20using%20PaLM-2%20models%2C%20we%20find%20that%20ReST%24%5E%7BEM%7D%24%20scales%20favorably%20with%20model%20size%20and%20significantly%20surpasses%20fine-tuning%20only%20on%20human%20data.%20Overall%2C%20our%20findings%20suggest%20self-training%20with%20feedback%20can%20substantially%20reduce%20dependence%20on%20human-generated%20data.)
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